Renewable energy generation is attracting considerable attention for a number of reasons. Firstly, renewable energy is considered the preferred alternative to fossil fuel based energy generation in order to combat greenhouse gas emissions such as CO2, which are held responsible for the changes to global climates. Secondly, with supplies of fossil fuels, and in particular oil, expected to run out in the foreseeable future, alternative energy supplies must be found for future generations.
Harvesting solar energy is one of the most promising ways of generating renewable energies, due to the abundant nature of the energy source, which is freely available across the globe. Over the last two decades, solar energy conversion has increasingly contributed to the generation of energy supplies all around the world. Typically, solar energy is converted into heat or into electricity, the latter typically being achieved by arrays of photovoltaic cells. The energy generated by such a photovoltaic system is typically stored in one or more batteries to make the electricity available on demand.
It is well-known that if a load is directly connected to the output of a photovoltaic system, the arrangement operates at a less than optimal efficiency. This is for instance explained in FIG. 1, in which the I-V curve (solid line) of the photovoltaic system at a given irradiation intensity is depicted together with such a load (dashed line). The system's operating point is determined by the intersection of the I-V curve with the load profile. In the FIG. 1, the operating point is at a lower power point instead of at the maximum power point (MPP), which is the point on the I-V curve where the product of the operating voltage and current, i.e. the output power is at a maximum.
For this reason, photovoltaic systems typically comprise a photovoltaic system controller that matches the load or impedance of the circuit connected to the photovoltaic cells in order to ensure that the photovoltaic system operates at the maximum power point by means of controlling the duty cycle of a DC voltage-DC voltage converter that converts the direct current voltage generated by photovoltaic cells into a further voltage. This, however, is not a trivial exercise as the maximum power point is not known a priori, and is subject to variation, for instance because of changes in irradiation intensity and/or temperature and/or partial shadowing. To this end, such controllers typically implement some algorithm that actively tracks the maximum power point of the photovoltaic system.
An overview of commonly used algorithms can be found in “Comparative Study of Maximum Power Point Tracking Algorithms” by D. P. Hohm and M. E. Ropp in Progress in Photovoltaics: Research and Applications, Vol. 11 (2003), pages 47-62. The most commonly used algorithms include Perturb and Observe (P&O), in which the power output of the photovoltaic array is monitored, and the duty cycle, e.g. the pulse width, of the DC voltage-DC voltage converter of the photovoltaic system controller is adjusted as a function of the monitored power output in order to move the operating voltage of the photovoltaic array as close as possible to its maximum power point.
A drawback of the P&O algorithm is that its convergence to the maximum power point can be slow, and that shifts in the maximum power point during execution of the algorithm can cause the power point of the photovoltaic system to oscillate around the maximum power point, or even diverge from the maximum power point.
Another commonly used algorithm is the incremental conductance (INC) algorithm, which monitors changes in the conductance of the photovoltaic system to adjust the duty cycle of the DC converter, based on the knowledge that at the maximum power point, the term dP/dV, i.e. the differential of the system power P with respect to its operating voltage V is zero. Although the INC algorithm has an improved performance compared to the P&O algorithm under rapidly changing irradiance, it is sensitive to noise and quantization errors, which can cause the system's operating point to oscillate around the maximum power point.
Yet another commonly used algorithm for MPP tracking is the constant voltage algorithm, which is based on the realization that the ratio between the maximum power voltage of the photovoltaic cell array and the open-circuit voltage is more or less constant for different I-V curves of the photovoltaic system. A drawback of this algorithm is that the MPP voltage is not always a fixed percentage of the open circuit voltage, as it depends on manufacturing process, quality of materials and other factors, and therefore requires frequent measurement of the open-circuit voltage in order to accurately track MPP, as the MPP can vary quite often due to changes in e.g. irradiance, temperature and partial shadowing. During the open-circuit voltage measurement, the battery of the photovoltaic system cannot be charged, i.e. the power output of the photovoltaic cell array is lost, thereby reducing the efficiency of the photovoltaic system.
Ali M Bazzi and Sami H Karaki, “Simulation of a New Maximum Power Point Tracking Technique for Multiple Photovoltaic Arrays”, discloses a two-stage maximum power point tracking (MPPT) technique for multiple photovoltaic arrays operating under different levels of irradiance and temperature. The first stage finds a point that bypasses local maxima and moves the operating point of the PV arrays near the global MPP. The second stage is a normal MPPT technique that finds the exact global maximum.